{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston  # 波士顿房价\n",
    "from sklearn.linear_model import LinearRegression  # 正规方程解\n",
    "from sklearn.linear_model import SGDRegressor  # 随机梯度下降线性回归\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error  # MSE\n",
    "from sklearn.metrics import mean_absolute_error  # MAE\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置字体\n",
    "plt.rcParams[\"font.sans-serif\"] = \"SimHei\"\n",
    "# 默认可以显示负号，增加字体显示后。需对负号正常显示进行设置\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集keys dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "boston = load_boston()\n",
    "print(\"数据集keys\", boston.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = boston.data, boston.target  # 特征和标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拆分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=1,\n",
    "                                                    # stratify=y,  # 回归类问题，没有分层参数\n",
    "                                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先拆分数据集 后标准化\n",
    "std = StandardScaler()\n",
    "std.fit(X_train)  # 使用训练集进行拟合【计算标准化和均值】\n",
    "X_train = std.transform(X_train)\n",
    "X_test = std.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr = LinearRegression()\n",
    "lr = SGDRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_test)  # 测试集进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差 23.517114005092196\n",
      "均方根误差RMSE 4.849444711004776\n",
      "平均绝对误差MAE 3.747293510388026\n",
      "R2 0.76203849831245\n"
     ]
    }
   ],
   "source": [
    "# 系数\n",
    "# print(\"系数\", lr.coef_)\n",
    "# print(\"截距\", lr.intercept_)\n",
    "# print(\"特征名称\", boston.feature_names)\n",
    "#\n",
    "# print(\"房价=\", end='')\n",
    "# for i, j in zip(boston.feature_names, lr.coef_):\n",
    "#     print(\"{}*{} + \".format(i, j), end=\" \")\n",
    "# print(lr.intercept_)\n",
    "\n",
    "# 系数最大是RM:房子的平均屋子数\n",
    "# 系数最小的NOX(负最大) 该值越大 房子越便宜，一氧化氮含量\n",
    "\n",
    "# 看效果是否好\n",
    "# 计算真实值和预测值之间的误差\n",
    "# 评估方法：均方误差 MSE、均方根误差RMSE、平均绝对误差MAE、R2\n",
    "# lr.score(X_test, y_test)\n",
    "\n",
    "print(\"均方误差\", mean_squared_error(y_test, y_pred))\n",
    "print(\"均方根误差RMSE\", np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print(\"平均绝对误差MAE\", mean_absolute_error(y_test, y_pred))\n",
    "print(\"R2\", lr.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "# 以样本编号为x轴；真实结果作为y轴\n",
    "x_data = np.arange(len(X_test))\n",
    "plt.plot(x_data, y_test)  # 真实结果\n",
    "plt.plot(x_data, y_pred)  #预测结果\n",
    "\n",
    "plt.legend([\"真实\", \"预测\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
